The present invention relates to data mapping for neuromorphic and synaptronic computational systems, and in particular, interconnecting peripheral devices for neuromorphic and synaptronic computational systems.
Neuromorphic and synaptronic computation, also referred to as artificial neural networks, are computational systems that permit electronic systems to essentially function in a manner analogous to that of biological brains. Neuromorphic and synaptronic computation do not generally utilize the traditional digital model of manipulating 0s and 1s. Instead, neuromorphic and synaptronic computation create connections between processing elements that are roughly functionally equivalent to neurons of a biological brain. Neuromorphic and synaptronic computation may comprise various electronic circuits that are modeled on biological neurons.
In biological systems, the point of contact between an axon of a neural module and a dendrite on another neuron is called a synapse, and with respect to the synapse, the two neurons are respectively called pre-synaptic and post-synaptic. The essence of our individual experiences is stored in conductance of the synapses. The synaptic conductance changes with time as a function of the relative spike times of pre-synaptic and post-synaptic neurons, as per spike-timing dependent plasticity (STDP). The STDP rule increases the conductance of a synapse if its post-synaptic neuron fires after its pre-synaptic neuron fires, and decreases the conductance of a synapse if the order of the two firings is reversed.
Neuromorphic devices communicate to personal computers (PCs) and peripheral devices via standard interfaces (e.g., universal serial bus (USB), peripheral component interconnect (PCI) using custom protocol converters, etc.). These converters translate the native protocol of neuromorphic chips (spikes) into the desired standard protocols (e.g., USB 2.0) and vice versa. Standard interfaces typically requires a host computer, such as a laptop computer in the loop. Therefore, the minimal neuromorphic system requires a laptop computer, a sensor and a neuromorphic board, which makes it difficult to create real-time systems; and power consumption (>5 W) prohibits embedded systems.